Logistic map: A possible random-number generator

Phatak, S. C.; Rao, S.

doi:10.1103/physreve.51.3670

Cited by 185 publications

(91 citation statements)

References 10 publications

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“…Therefore, the resulting series cannot be described as truly random, but as pseudo-random and its output has long been proposed as a pseudo-random number generator. Ulam and von Neumann (1947) were the first to examine this, and it has been successfully used in that capacity by several researchers (Patidar et al 2009;Phatak and Rao 1995). The probability-density distribution of the Logistic Map, as given by Eq.…”

Section: The Chaotic Networkmentioning

confidence: 99%

Applications and design of cooperative multi-agent ARN-based systems

et al. 2014

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Section: The Chaotic Networkmentioning

confidence: 99%

Applications and design of cooperative multi-agent ARN-based systems

et al. 2014

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“…However, when the messages are encrypted, their entropy should ideally be 8, certain degree of predictability, which threatens its security [14]. We apply the entropy on cipher image encryption using the logistic map.…”

Section: =0mentioning

confidence: 99%

Fast Dynamic Random Numbers Generator and Applications in Cryptosystems (FDRNG)

Ali¹,

Hasan²,

Abdulah³

et al. 2017

DJPS

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Security is one of the significant challenges that people are faced over the entire world in every aspect of their lives. One of the methods used in security areas is cryptography. In this work we have investigated the possibility of using the multi logistic maps in the dynamical matrix for pseudo random number generator. Theoretical analysis and experimental show the sequences generated by the proposed random number generated possess many good properties. The proposed can be used in many applications requiring random binary sequences and also in the design of secure cryptosystems.

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“…, [288]G} generates all the elements of EC over F 2 8 , hence the given elliptic curve group is cyclic. In the case of C-D ECPRNG, we use the Logistic map [25] as our chaotic map to generate the random bits b i defined in (9).…”

Section: Implementation Examplementioning

confidence: 99%

Pseudo-random Sequence Generation from Elliptic Curves over a Finite Field of Characteristic 2

Reyad

Kotulski

2016

Annals of Computer Science and Information Systems

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Abstract-In this paper, the randomness of binary sequences generated from elliptic curves over a finite field of characteristic 2 is studied. A scheme of construction based on the ChaosDriven Elliptic Curve Pseudo-random Number Generator (C-D ECPRNG) is proposed. The generators based of this scheme are verified by using tests from the NIST Statistical Test Suite to analyze their statistical properties. An elliptic curve used in the numerical example is defined over F 2 8 . The investigations which made for the generated series of two output sequences of the lengths of 2 10 and 2 20 bits shown that 14 generators working according to our general scheme exhibit good randomness properties. Next, the binary sequences generated by these 14 schemes were used for encrypting a 256 × 256 grayscale Lena image as an application example and the security analysis of the ciphered images was carried out.

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Logistic map: A possible random-number generator

Cited by 185 publications

References 10 publications

Applications and design of cooperative multi-agent ARN-based systems

Applications and design of cooperative multi-agent ARN-based systems

Fast Dynamic Random Numbers Generator and Applications in Cryptosystems (FDRNG)

Pseudo-random Sequence Generation from Elliptic Curves over a Finite Field of Characteristic 2

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